46,119 research outputs found
Range imager performance comparison in homodyne and heterodyne operating modes
Range imaging cameras measure depth simultaneously for every pixel in a given field of view. In most implementations the basic operating principles are the same. A scene is illuminated with an intensity modulated light source and the reflected signal is sampled using a gain-modulated imager. Previously we presented a unique heterodyne range imaging system that employed a bulky and power hungry image intensifier as the high speed gain-modulation mechanism. In this paper we present a new range imager using an internally modulated image sensor that is designed to operate in heterodyne mode, but can also operate in homodyne mode. We discuss homodyne and heterodyne range imaging, and the merits of the various types of hardware used to implement these systems. Following this we describe in detail the hardware and firmware components of our new ranger. We experimentally compare the two operating modes and demonstrate that heterodyne operation is less sensitive to some of the limitations suffered in homodyne mode, resulting in better linearity and ranging precision characteristics. We conclude by showing various qualitative examples that demonstrate the system’s three-dimensional measurement performance
Toward-1mm depth precision with a solid state full-field range imaging system
Previously, we demonstrated a novel heterodyne based solid-state full-field range-finding imaging system. This system is comprised of modulated LED illumination, a modulated image intensifier, and a digital video camera. A 10 MHz drive is provided with 1 Hz difference between the LEDs and image intensifier. A sequence of images of the resulting beating intensifier output are captured and processed to determine phase and hence distance to the object for each pixel. In a previous publication, we detailed results showing a one-sigma precision of 15 mm to 30 mm (depending on signal strength). Furthermore, we identified the limitations of the system and potential improvements that were expected to result in a range precision in the order of 1 mm. These primarily include increasing the operating frequency and improving optical coupling and sensitivity. In this paper, we report on the implementation of these improvements and the new system characteristics. We also comment on the factors that are important for high precision image ranging and present configuration strategies for best performance. Ranging with sub-millimeter precision is demonstrated by imaging a planar surface and calculating the deviations from a planar fit. The results are also illustrated graphically by imaging a garden gnome
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
Random on-board pixel sampling (ROPS) X-ray Camera
Recent advances in compressed sensing theory and algorithms offer new
possibilities for high-speed X-ray camera design. In many CMOS cameras, each
pixel has an independent on-board circuit that includes an amplifier, noise
rejection, signal shaper, an analog-to-digital converter (ADC), and optional
in-pixel storage. When X-ray images are sparse, i.e., when one of the following
cases is true: (a.) The number of pixels with true X-ray hits is much smaller
than the total number of pixels; (b.) The X-ray information is redundant; or
(c.) Some prior knowledge about the X-ray images exists, sparse sampling may be
allowed. Here we first illustrate the feasibility of random on-board pixel
sampling (ROPS) using an existing set of X-ray images, followed by a discussion
about signal to noise as a function of pixel size. Next, we describe a possible
circuit architecture to achieve random pixel access and in-pixel storage. The
combination of a multilayer architecture, sparse on-chip sampling, and
computational image techniques, is expected to facilitate the development and
applications of high-speed X-ray camera technology.Comment: 9 pages, 6 figures, Presented in 19th iWoRI
Temporal phase unwrapping using deep learning
The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical
phase unwrapping algorithm for fringe projection profilometry (FPP), is capable
of eliminating the phase ambiguities even in the presence of surface
discontinuities or spatially isolated objects. For the simplest and most
efficient case, two sets of 3-step phase-shifting fringe patterns are used: the
high-frequency one is for 3D measurement and the unit-frequency one is for
unwrapping the phase obtained from the high-frequency pattern set. The final
measurement precision or sensitivity is determined by the number of fringes
used within the high-frequency pattern, under the precondition that the phase
can be successfully unwrapped without triggering the fringe order error.
Consequently, in order to guarantee a reasonable unwrapping success rate, the
fringe number (or period number) of the high-frequency fringe patterns is
generally restricted to about 16, resulting in limited measurement accuracy. On
the other hand, using additional intermediate sets of fringe patterns can
unwrap the phase with higher frequency, but at the expense of a prolonged
pattern sequence. Inspired by recent successes of deep learning techniques for
computer vision and computational imaging, in this work, we report that the
deep neural networks can learn to perform TPU after appropriate training, as
called deep-learning based temporal phase unwrapping (DL-TPU), which can
substantially improve the unwrapping reliability compared with MF-TPU even in
the presence of different types of error sources, e.g., intensity noise, low
fringe modulation, and projector nonlinearity. We further experimentally
demonstrate for the first time, to our knowledge, that the high-frequency phase
obtained from 64-period 3-step phase-shifting fringe patterns can be directly
and reliably unwrapped from one unit-frequency phase using DL-TPU
Roadmap on optical security
Postprint (author's final draft
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